Method and apparatus for determining and assessing chamber inconsistency in a tool

ABSTRACT

A method is disclosed to determine and assess chamber inconsistency in a multi-chambered tool, especially a multi-chambered tool involved in mass production processes. Wafers produced by the tool are grouped in lots measured to obtain loss yield groups. The invention sorts yield losses to obtain a corresponding monotonic sequence. The invention then averages the monotonic sequences. If the resulting mean monotonic sequence fits with a predetermined aberration, the tool is determined to suffer from chamber inconsistency.

CROSS REFERENCE TO RELATED APPLICATION

This is a divisional application of U.S. patent application Ser. No.09/912,682, entitled “METHOD AND APPARATUS FOR DETERMINING AND ASSESSINGCHAMBER INCONSISTENCY IN A TOOL” filed Jul. 24, 2001 now U.S. Pat. No.6,537,834.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a method and apparatus for determiningand assessing chamber inconsistency in a tool. In particular, thepresent invention relates to determining and assessing chamberinconsistency by processing the measurement data obtained by measuringthe units of a product.

2. Description of the Related Art

Semiconductor fabrication normally encompasses hundreds of operations toprocess a blank wafer for the generation of logic integrated circuitry(IC), dynamic random access memory (DRAM), static random access memory,flash memory, and other chips. Each operation responds for a singleprocess, such as lithography, etching, deposition, and oxidation. Aspart of mass-production, each operation usually has several tools thatcan execute a corresponding operation. Each tool is usually designed tohave multiple chambers to process the same operation in parallel.Theoretically, a single operation needs to produce the same processresults. Thus, each chamber of a single tool should produce equal andidentical results.

In semiconductor fabrication, yield, whether wafer yield or chip yield,signifies the cost and profit of a fabrication process. In order tomaximize yield, the most unproductive operation or tool is firstlocated, and then proper action, such as adjustment or repair, must beperformed. Incidents, however, in which production by one or morechambers of a tool differs significantly form that of the otherchambers, defined as chamber inconsistency, is very hard to identify.

For the purpose of convenient transportation and management, wafers insemiconductor fabrication are usually gathered in lots. Presently,8-inch fabrication puts 25 wafers in a cassette, referred to as a lot.The cassette bears a lot number. Furthermore, each wafer has a wafernumber marked on its surface. For example, the wafer number 05 in thelot “65038” means that the corresponding wafer is the 5^(th) wafer inthe lot numbered 65038. Lots may not be full, that is, having fewer than25 wafers. During operations, some wafers in a lot may be abandoned dueto misoperation. Therefore, the number of wafers in a lot can beanywhere from 1 to 25.

Determination of this chamber inconsistency faces the followingimpediments:

1. Chamber inconsistency is not identifiable from commonly accesseddata, such as average yield or average bit loss, of lots.

2. Wafer numbers have no fixed correlation to the chambers of a tool.Each wafer is randomly chosen to be processed in a chamber. Chamberinconsistency thus cannot be identified by checking wafers with the samewafer number.

SUMMARY OF THE INVENTION

An object of the present invention is to determine and assess whether atool with multiple chambers has chamber inconsistency.

The method for determining chamber inconsistency of a single toolaccording to the present invention is applied to a process flow formanufacturing a product. The tool has a plurality of process chambers. Aplurality of product groups is provided. Each the product groups has aplurality of units and has been processed in the process chambers of thetool. The product groups are measured to obtain measurement data groups.Each measurement data group comprises a plurality of measured datacorresponding to the units in a corresponding product group. Themeasured data of the corresponding product group are sorted to obtain acorresponding monotonic sequence. The monotonic sequences correspondingto the product groups are element-by-element averaged to obtain a meanmonotonic sequence. If the mean monotonic sequence fits in with apredetermined criterion, the tool is indicated to have chamberinconsistency.

The product groups and the units can be lots and wafers, respectively.The measured data can be resistance, capacitance, chip yield or failedbit number of a lot.

The predetermined criterion, for example, can be that the mean monotonicsequence has a step variation in a medial position, or that the meanmonotonic sequence is out of a predetermined distribution.

This invention further provides a method for determining chamberinconsistency in several tools. First, the mean monotonic sequencescorresponding to the tools are ascertained according to the presentinvention. The chamber inconsistency of the tools can be determined bychecking these mean monotonic sequences.

Except via step variation and predetermined distribution, a tool can bedetermined to have chamber inconsistency if its corresponding meanmonotonic sequence is unique among these mean monotonic sequences.

The present invention further provides an apparatus for determiningchamber inconsistency in a tool. This apparatus is applied to a processflow for manufacturing a product. The tool has a plurality of processchambers. The apparatus comprises a plurality of product groups,measurement data provider, a sorter, a means for averaging and a systemof analysis. Each the product groups has a plurality of units of theproduct and has been processed in the process chambers of the tool. Themeasurement data provider provides measurement data groups, eachcomprising a plurality of measured data obtained by measuring the unitsin a corresponding product group. The sorter sorts the measured data ofthe corresponding product group to obtain a corresponding monotonicsequence. The monotonic sequences corresponding to the product groupsare averaged, element by element, to obtain a mean monotonic sequence.The system of analysis indicates that the tool has chamber inconsistencyif the mean monotonic sequence fits in with a predetermined criterion.

The advantage of the present invention is that the chamber inconsistencyin a tool is clearly and precisely determined so proper action can bequickly taken to improve the yield, thereby increasing profits.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention can be more fully understood by reading thesubsequent detailed description in conjunction with the examples andreferences made to the accompanying drawings, wherein:

FIG. 1 depicts a tool with chamber inconsistency;

FIG. 2A depicts the yield losses of 12 wafers in the lot process in thetool of FIG. 1, arranged according to the wafer number;

FIG. 2B depicts the sorted result of the yield losses of the wafers inFIG. 2A;

FIG. 3 depicts a multiple-chamber tool without chamber inconsistency;

FIG. 4A depicts the yield losses of 12 wafers in the lot process in thetool of FIG. 3, arranged according to the wafer number;

FIG. 4B depicts the sorted result of the yield losses of the wafers inFIG. 4A;

FIG. 5 shows the monotonic sequences in FIGS. 2B and 4B;

FIG. 6A illustrates the flowchart for determining chamber inconsistencyof a possibly inconsistent tool;

FIG. 6B illustrates the flowchart for generating the mean monotonicsequence in FIG. 6A; and

FIG. 7 illustrates 3 curves for representing 3 mean monotonic sequences.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The essence of the present invention mainly has three steps: the firststep is sorting, lot by lot, the measurement data of the lots processedby a possibly inconsistent tool to obtain monotonic sequences; thesecond step is averaging the monotonic sequences to obtain a meanmonotonic; and the third step is determining and assessing whether thetool has chamber inconsistency according to the mean monotonic sequence.

FIG. 1 depicts a tool having chamber inconsistency. Chamber C is shownas to indicate it is inconsistent with the other three chambers. Thewafers processed in chamber C will have significant differences inperformance in comparison with the wafers processed in other chambers.Yield loss, a kind of measured data, is used as an example of theperformance of a wafer, but does not limit the application of thepresent invention.

FIG. 2A depicts the yield losses of 12 wafers in the lot process in thetool of FIG. 1, arranged according to the wafer number. Wafers W₁, W₅and W₉ have been processed in chamber C, and, thus, have distinguishableyield losses. Other wafers, such as W₂, W₃, W₇, having been processed inchamber A, B or C, have smaller yield losses.

FIG. 2B depicts the sorted result of the yield losses of the wafers inFIG. 2A. The yield losses in FIG. 2B are arranged from large to small.The yield losses of W₁, W₅ and W₉ are larger and they, therefore, arearranged in the front ones. The yield losses of other wafers are smallerand are arranged in the rear ones. Such arrangement of yield lossesconstructs a monotonic sequence. In FIG. 2B, a significant stepvariation (abrupt change) can be found in the location between the3^(rd) element and the 4^(th) element, and is utilized to determine thechamber inconsistency of the tool according to the present invention.

As a comparable example, FIG. 3 depicts a multiple-chamber tool withoutchamber inconsistency. The chambers (A, B, C and D) of the tool in FIG.3 are about the same and generate similar process results.

FIG. 4A depicts the yield losses of 12 wafers in the lot process in thetool of FIG. 3, arranged according to the wafer number. As mentioned inthe last paragraph, all the yield losses are similar due to thesimilarity of the 4 chambers in FIG. 3.

FIG. 4B depicts the sorted result of the yield losses of the wafers inFIG. 4A. After sorting, FIG. 4B shows a monotonic sequence steadydecreased as the number of the element increases. In other words, thereis no step variation (abrupt change) within the monotonic sequence inFIG. 4B.

FIG. 5 shows the monotonic sequences in FIGS. 2B and 4B, where the curvemarked by circles represents the monotonic sequence in FIG. 2B while theone marked by triangles represents the monotonic sequence in FIG. 4B. Bycomparing the two curves in FIG. 5, one can find that the mostdifference between them is the occurrence of the step variation. Thecurve marked by circles (corresponding to a tool with chamberinconsistency) has a significant step variation between the 3^(rd)element and the 4^(th) element. Nevertheless, the curve marked bytriangle (corresponding to a tool without chamber inconsistency) has nostep variation therein. The criterion for deciding variation as stepvariation is predetermined by engineers, dependent upon product, processability, experience, requirement, and other suitable parameters. Theoccurrence of step variation is the key point for determining andassessing whether a tool has chamber inconsistency in this invention.

The measured data from a single lot is not enough to determine whether atool has chamber inconsistency or not since what happens to the singlelot may be an accidental event. Thus, the present invention applies theaverage skill of statistics. The monotonic sequences of correspondinglots having been processed by a tool can be generated according to theprevious paragraphs. By averaging, element by element, the monotonicsequences, a mean monotonic sequence is generated to represent theperformance of the corresponding tool. By examining the mean monotonicsequence, whether the corresponding tool has chamber inconsistency canbe determined.

FIG. 6A illustrates the flowchart for determining and assessing chamberinconsistency in a possibly inconsistent tool.

First, at least one possibly inconsistent tool, which is expected tohave inconsistent chamber(s) in view of production, is selected (symbol10). This step can be made by experienced engineers or by computerprograms. For example, when the failed dies (or chips) during testinghave specific failure bins or construct a specific pattern, experiencedengineers can choose at least one tool as a possibly inconsistent toolwhich contributes to the failure according to their work experience.

The mean monotonic sequence corresponding to the possibly inconsistenttool is then generated (symbol 12). This mean monotonic sequence, whoseorigination will be explained later, represents the statisticperformance of the wafers in a lot processed by the possiblyinconsistent tool.

Whether the possibly inconsistent tool has chamber inconsistency is thendetermined according the mean monotonic sequence (symbol 14). Forexample, if there is a step variation within the mean monotonicsequence, the possibly inconsistent tool has chamber inconsistency.Another criterion for determining chamber inconsistency is described asfollows. The mean monotonic sequence for a tool without chamberinconsistency should have an appearance of an almost smooth curve, suchas the curve marked by triangles in FIG. 5. By collecting the meanmonotonic sequences corresponding to the tools provably without chamberinconsistency, one can conclude that a tool without chamberinconsistency must correspond to a mean monotonic sequence fitting witha certain mathematical equation, which can be found by statistics. If apossibly inconsistent tool has a mean monotonic sequence being out of apredetermined distribution determined by the certain mathematicalequation, the possibly inconsistent tool can be determined or assessedto have chamber inconsistency. Furthermore, several possiblyinconsistent tools can correspond to generate mean monotonic sequences.If one of the mean monotonic sequences is unique to the others, thecorresponding possibly inconsistent tool has chamber inconsistency,since the tools with chamber inconsistency are usually rare incomparison with hundreds of tools in a fabrication operation. Thedescribed criteria can be achieved by software programming.

FIG. 6B illustrates the flowchart for generating the mean monotonicsequence in FIG. 6A.

The lots processed by the possibly inconsistent tool are retrieved(symbol 16). In order to make sure that each retrieved lot is a commonrepresentative of the performance of the possibly inconsistent tool,each the retrieved lots is suggested to have wafers more than a certainnumber, for example, over 80% of the maximum capacity of a lot. By now,the 80% of the maximum capacity of a lot in an 8-inch wafer fabricationis equal to 20.

The wafers of the retrieved lots are measured to obtain measured data(symbol 18), such as yield losses. For instance, if there are 21 wafersin a retrieved lot, 21 corresponding yield losses are collected.

The measured data of the retrieved lots are sorted lot by lot to obtaincorresponding monotonic sequences (symbol 20). For example, the 21 yieldlosses of a lot are sorted to construct a monotonic sequence with 21elements. All the monotonic sequences are either increasing sequences ordecreasing sequences.

All the monotonic sequences are averaged element by element to generatea mean monotonic sequence (symbol 22). The 1^(st) element of the meanmonotonic sequence is obtained by averaging all the 1^(st) elements inthe monotonic sequences. The 2^(nd) element of the mean monotonicsequence is obtained by averaging all the 2^(nd) elements in themonotonic sequences, and so on. Due to the different element numbers indifferent monotonic sequences, one can 1) ignore the shorter monotonicsequences to generate the last elements of the mean monotonic sequenceby averaging the monotonic sequences with last elements; 2) limit thelength of the mean monotonic sequence to be equal to that of theshortest monotonic sequence(s); and 3) set a fixed value to the rearempty elements in the shorter monotonic sequences and obtain the meanmonotonic sequence. The fixed value is the smallest possible value ifthe monotonic sequences are decreasing sequences, vice versa.

FIG. 7 illustrates 3 curves for representing 3 mean monotonic sequences.In FIG. 7, curves 30 and 34 have no step variation therein. However,curve 32 has a step variation in a medial position, therefore the toolcorresponding to the curve 32 can be determined to have chamberinconsistency. Curves 30 and 34 are very similar to an exponentialcurve, such as Ae^(-Bx), which is a non-match for curve 32. Therefore,the tool corresponding to curve 32 can be determined to have chamberinconsistency according to its incompatibility with the mathematicalequation.

The medial position refers to the position not located within the firstseveral elements or the last several elements. The detail explanationfollows. If, for example, most of the retrieved lots have 25 wafers in alot, and the tool with chamber inconsistency has a total of two chambersbut one inconsistent chamber therein, and the location of the stepvariation of the mean monotonic sequence should be around the13^(th)(=25*½) element. By the same theory, if most of the retrievedlots have 25 wafers per lot, and the tool with chamber inconsistency hasa total of five chambers, the location of the step variation of the meanmonotonic sequence should be around the 5^(th), 10^(th), 15^(th) or20^(th), respectively corresponding to the total number 1, 2, 3 or 4 ofthe inconsistent chamber(s). That is, the mean monotonic sequence, orthe location of the step variation, can indicate the total number of theinconsistent chamber(s) or the ratio of the inconsistent chamber(s) tothe total chambers. The majority of tools have 5 chambers or fewer.Therefore, a step variation can be ignored if it occurs at the locationprior the 5^(th) element or later the 20^(th) element.

Of course, as well as using a yield loss as measured data, any one ofthe measured data representing the characteristic of a wafer can beutilized in this invention. For example, a yield loss, a failed bitnumber, an average N-well resistance, a PN junction capacitance of awafer.

In addition to semiconductor fabrication, the present invention can alsoemployed to other kinds of mass-production for different products,thereby determining and assessing chamber inconsistency in anymulti-chambered tool.

Finally, while the invention has been described by way of examples andin terms of the preferred embodiments, it is to be understood that theinvention is not limited to the disclosed embodiments. On the contrary,it is intended to cover various modifications and similar arrangementsas would be apparent to those skilled in the art. Therefore, the scopeof the appended claims should be accorded the broadest interpretation soas to encompass all such modifications and similar arrangements.

What is claimed is:
 1. A apparatus for determining chamber inconsistencyin a tool, applied to a process flow for manufacturing a product, thetool having a plurality of process chambers, comprising: a plurality ofproduct groups, each the product groups having a plurality of units ofthe product and having been processed in the process chambers of thetool; measurement data provider for providing measurement data groups,each measurement data group comprising a plurality of measured dataobtained by measuring the units in a corresponding product group; asorter for sorting the measured data of the corresponding product groupto obtain an corresponding monotonic sequence; a means for averaging,element by element, the monotonic sequences corresponding to the productgroups to obtain a mean monotonic sequence; and a system of analysisfor, if the mean monotonic sequence fits in with a predeterminedcriterion, indicating the tool has chamber inconsistency.
 2. Theapparatus as claimed in claim 1, wherein the predetermined criterion isthat the mean monotonic sequence has a step variation in a medialposition.
 3. The apparatus as claimed in claim 1, wherein thepredetermined criterion is that the mean monotonic sequence is out of apredetermined distribution.
 4. The apparatus as claimed in claim 1,wherein the product groups are lots comprising a plurality of wafers. 5.The apparatus as claimed in claim 1, wherein each lot has a maximumcapacity of 25 wafers.
 6. The apparatus as claimed in claim 4, whereineach lot has a maximum capacity and the wafers in each lot have a totalnumber larger than a predetermined ratio of the maximum capacity.